robust pca
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Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7185
Author(s):  
Samira Ebrahimi ◽  
Julien R. Fleuret ◽  
Matthieu Klein ◽  
Louis-Daniel Théroux ◽  
Clemente Ibarra-Castanedo ◽  
...  

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.


2021 ◽  
pp. 278-285
Author(s):  
Haiyun Zhang ◽  
Jian Dong ◽  
Jiancheng Zhou ◽  
Li Zhang ◽  
Pengjun Hu ◽  
...  

2021 ◽  
Vol 49 (5) ◽  
Author(s):  
Yuxin Chen ◽  
Jianqing Fan ◽  
Cong Ma ◽  
Yuling Yan

2021 ◽  
Author(s):  
Yang Liu ◽  
Qian Zhang ◽  
Yongyong Chen ◽  
Qiang Cheng ◽  
Chong Peng

2021 ◽  
Author(s):  
Yihui Sui ◽  
Shaoyuan Yan ◽  
Jiaqi Zang ◽  
Xin Liu ◽  
Dean Ta ◽  
...  

2021 ◽  
Author(s):  
Kailiang Xu ◽  
Xingyi Guo ◽  
Yihui Sui ◽  
Vincent Hingot ◽  
Olivier Couture ◽  
...  

2021 ◽  
Vol 19 ◽  
pp. 546-551
Author(s):  
Francisco M. Arrabal-Campos ◽  
◽  
Alfredo Alcayde ◽  
Francisco G. Montoya ◽  
Juan Martínez-Lao ◽  
...  

Author(s):  
Zoe Doyle ◽  
Daehyun Yoon ◽  
Philip K. Lee ◽  
Jarrett Rosenberg ◽  
Brian A. Hargreaves ◽  
...  

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